Context and objectives
In Flanders, fruit-growing is an important economic activity. Fruit orchard productivity monitoring becomes more important than ever due to the increasing demand for uniform batches of quality fruits, and the reduced use of pesticides and fertilizers for environmental sustainability. The common way to acquire tree productivity information is through visual inspections, which are subjective, demanding and time consuming. In many sectors visual inspection is already replaced by computer vision. Image processing seems the best option to automate the visual inspections in orchards as well. The ‘optimal’ plant vitality remains a theoretical concept. However, plants under stress differ in some characteristics (e.g., biomass, LAI, biochemical parameter content, and photosynthetic efficiency) and these characteristics can be considered as indices of productivity.
The project aims at developing an optimized orchard tree productivity monitoring system by integrating multi-date and multi-sensor remote sensing (RS) and in-situ data. This provides the opportunity to increase profitability and reduce the environmental effects of farming by matching the application of inputs such as pesticides and fertilizers with actual conditions in the field.
The needs of the farmer will be addressed, i.e.:
• Which parcels do need specific management actions (a.o., irrigation, fertilization)?
• What are the yield expectations per management zone?
• How many people to hire for harvest?
• How to maximize profit? Reduce the inputs by variable rate irrigation and fertigation?
• How to reduce the environmental effects of pesticides and fertilizers?
This project aims at acquiring detailed tree productivity information in fruit orchards by integrating multidate and multisensor remote sensing data. We aim at providing an accurate productivity map based on:
- An improved UAV preprocessing chain,
- An object-based hierarchical multimodal image analysis technique, and
- A dedicated multitemporal classification method.
Expected scientific results
- Ground truth vitality maps and yield maps
- Improved geometric corrected orthophotos for fusion purposes
- Improved geometric corrected orthophotos for multidate analysis purposes
- Vitality parameter maps based on single date UAV imagery
- Vitality parameter maps based on single date APEX imagery
- Multi date, multi sensor productivity modelling
- Prediction of tree productivity based on single vs multidate multisensor image data
Expected products and services
- Improved geometric preprocessing of UAV data
- Improved prediction of tree productivity
- Improved image processing chain by integrating new algorithms
PcFruit and its members, Fruit Auction, scientific users
|Project leader(s):||VITO - Remote Sensing - Teledetectie en aardobservatieprocessen|